命名实体和子类型标记的混合方法

Applied Natural Language Processing Conference Pub Date : 2000-04-29 DOI:10.3115/974147.974181

R. Srihari

{"title":"命名实体和子类型标记的混合方法","authors":"R. Srihari","doi":"10.3115/974147.974181","DOIUrl":null,"url":null,"abstract":"This paper presents a hybrid approach for named entity (NE) tagging which combines Maximum Entropy Model (MaxEnt), Hidden Markov Model (HMM) and handcrafted grammatical rules. Each has innate strengths and weaknesses; the combination results in a very high precision tagger. MaxEnt includes external gazetteers in the system. Sub-category generation is also discussed.","PeriodicalId":103133,"journal":{"name":"Applied Natural Language Processing Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"142","resultStr":"{\"title\":\"A Hybrid Approach for Named Entity and Sub-Type Tagging\",\"authors\":\"R. Srihari\",\"doi\":\"10.3115/974147.974181\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a hybrid approach for named entity (NE) tagging which combines Maximum Entropy Model (MaxEnt), Hidden Markov Model (HMM) and handcrafted grammatical rules. Each has innate strengths and weaknesses; the combination results in a very high precision tagger. MaxEnt includes external gazetteers in the system. Sub-category generation is also discussed.\",\"PeriodicalId\":103133,\"journal\":{\"name\":\"Applied Natural Language Processing Conference\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-04-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"142\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Natural Language Processing Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3115/974147.974181\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Natural Language Processing Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3115/974147.974181","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 142

摘要

本文提出了一种将最大熵模型(MaxEnt)、隐马尔可夫模型(HMM)和手工语法规则相结合的命名实体(NE)标注混合方法。每个人都有先天的优点和缺点;这种组合产生了非常高精度的标签机。MaxEnt在系统中包括外部地名词典。还讨论了子类别的生成。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

A Hybrid Approach for Named Entity and Sub-Type Tagging

This paper presents a hybrid approach for named entity (NE) tagging which combines Maximum Entropy Model (MaxEnt), Hidden Markov Model (HMM) and handcrafted grammatical rules. Each has innate strengths and weaknesses; the combination results in a very high precision tagger. MaxEnt includes external gazetteers in the system. Sub-category generation is also discussed.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Applied Natural Language Processing Conference

自引率

0.00%

发文量